Text Generation
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Text Generation is the task of generating text with the goal of appearing indistinguishable to human-written text. This task is more formally known as "natural language generation" in the literature.
Text generation can be addressed with Markov processes or deep generative models like LSTMs. Recently, some of the most advanced methods for text generation include BART, GPT and other GAN-based approaches. Text generation systems are evaluated either through human ratings or automatic evaluation metrics like METEOR, ROUGE, and BLEU.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Adversarial Ranking for Language Generation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zhang0jhon/AttentionOCR
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/zhangbo2008/best_pytorch_transformer
https://github.com/zhanghainan/RNN-S2S
https://github.com/zhanghainan/RNN-encdec
https://github.com/zhanghainan/ReinforcingCoherence
https://github.com/zhanghainan/TailoredSeq2Seq2DifferentConversationScenarios
https://github.com/zhanghainan/s2s-with-attention
https://github.com/zhanglab-aim/lingo
AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
https://github.com/zhangxu0963/npc
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/zhangzwwww/dietcode
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
https://github.com/zhangzwwww/dietcodebert
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
https://github.com/zhanyang-nwpu/rsvg-pytorch
https://github.com/zhaoolee/garss
Position: It's Time to Act on the Risk of Efficient Personalized Text Generation
https://github.com/zhdbwe/Paper-DailyReading
https://github.com/zhijing-jin/genwiki
https://github.com/zhiminzhang0830/D3PM_Paddle
Structured Denoising Diffusion Models in Discrete State-Spaces
https://github.com/zhyack/UDRG
A Semi-Supervised Approach for Low-Resourced Text Generation
https://github.com/zimengq/PyTorch-ReCode
A Syntactic Neural Model for General-Purpose Code Generation
https://github.com/zipeiyang/DeepSpeechRecognition
https://github.com/zpschang/seqGAN
https://github.com/zsweet/BERT_zsw
https://github.com/zysite/post
https://github.com/zzmtsvv/adversarial
https://github.com/zzxslp/wcl
Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation
https://gitlab.com/bpaassen/ast2vec
https://gitlab.com/inpefess/ast2vec
https://gitlab.com/raymondhs/char-rnn-truecase
https://gitlab.com/shimorina/inlg-2018
https://gitlab.com/shimorina/webnlg-dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.